Abstract
Various modeling approaches have been suggested for the modeling and simulation of gasification processes. These models allow for the prediction of gasifier performance at different conditions and using different feedstocks from which the system parameters can be optimized to design efficient gasifiers. Complex models require significant time and effort to develop, and they might only be accurate for use with a specific catalyst. Hence, various simpler models have also been developed, including thermodynamic equilibrium models and empirical models, which can be developed and solved more quickly, allowing such models to be used for optimization. In this study, linear and quadratic expressions in terms of the gasifier input value parameters are developed based on linear regression. To identify significant parameters and reduce the complexity of these expressions, a LASSO (least absolute shrinkage and selection operator) shrinkage method is applied together with cross validation. In this way, the significant parameters are revealed and simple models with reasonable accuracy are obtained.
Highlights
The gasification of biomass allows for the production of syngas, consisting of hydrogen and carbon monoxide, which can be used as fuel or converted to other products
The review of Patra and Sheth mentions several categories of model biomass gasifiers including more complex models based on kinetic rate expressions or computational fluid dynamics, in addition to relatively simpler models based on thermodynamic equilibrium assumptions and empirical models based on artificial neural networks [1]
Both linear and quadratic expressions are considered, and a model reduction method is implemented based on cross validation with the LASSO method in order to select subsets of important parameters so that the resulting expressions can be simplified
Summary
The gasification of biomass allows for the production of syngas, consisting of hydrogen and carbon monoxide, which can be used as fuel or converted to other products. Pan and Pandey have shown that both linear and quadratic expressions give high relative errors when they try to fit them to data for fluidized bed gasifiers fed with municipal solid waste [18] They show that an artificial neural network and their proposed Bayesian approach using Gaussian processes can achieve a much more accurate prediction, the main aim of their proposed method is to incorporate uncertainty [18]. While dimension reducing model reduction has been successfully applied (e.g., using principal component analysis) to identify significant parameters [15,16,17], the use of the LASSO [19] (least absolute shrinkage and selection operator) shrinkage method, which aims to eliminate large numbers of less significant parameters, has not so far been applied for the model reduction of biomass gasification models In this study, both linear and quadratic expressions are fitted to a set of data from a downdraft biomass gasifier. The resulting models are evaluated based on their ability to predict the gasifier output
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
